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Robust Fréchet Mean and PGA on Riemannian Manifolds with Applications to Neuroimaging Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Predicting Interrelated Alzheimer’s Disease Outcomes via New Self-learned Structured Low-Rank Model

Authors : Xiaoqian Wang, Kefei Liu, Jingwen Yan, Shannon L. Risacher, Andrew J. Saykin, Li Shen, Heng Huang, for the ADNI

Published in: Information Processing in Medical Imaging

Publisher: Springer International Publishing

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Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. As the prodromal stage of AD, Mild Cognitive Impairment (MCI) maintains a good chance of converting to AD. How to efficaciously detect this conversion from MCI to AD is significant in AD diagnosis. Different from standard classification problems where the distributions of classes are independent, the AD outcomes are usually interrelated (their distributions have certain overlaps). Most of existing methods failed to examine the interrelations among different classes, such as AD, MCI conversion and MCI non-conversion. In this paper, we proposed a novel self-learned low-rank structured learning model to automatically uncover the interrelations among classes and utilized such interrelated structures to enhance classification. We conducted experiments on the ADNI cohort data. Empirical results demonstrated advantages of our model.

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Metadata
Title
Predicting Interrelated Alzheimer’s Disease Outcomes via New Self-learned Structured Low-Rank Model
Authors
Xiaoqian Wang
Kefei Liu
Jingwen Yan
Shannon L. Risacher
Andrew J. Saykin
Li Shen
Heng Huang
for the ADNI
Copyright Year
2017
Book
Information Processing in Medical Imaging

Print ISBN: 978-3-319-59049-3

Electronic ISBN: 978-3-319-59050-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59050-9_16

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